My first experience with Lemmy was thinking that the UI was beautiful, and lemmy.ml (the first instance I looked at) was asking people not to join because they already had 1500 users and were struggling to scale.
1500 users just doesn’t seem like much, it seems like the type of load you could handle with a Raspberry Pi in a dusty corner.
Are the Lemmy servers struggling to scale because of the federation process / protocols?
Maybe I underestimate how much compute goes into hosting user generated content? Users generate very little text, but uploading pictures takes more space. Users are generating millions of bytes of content and it’s overloading computers that can handle billions of bytes with ease, what happened? Am I missing something here?
Or maybe the code is just inefficient?
Which brings me to the title’s question: Does Lemmy benefit from using Rust? None of the problems I can imagine are related to code execution speed.
If the federation process and protocols are inefficient, then everything is being built on sand. Popular protocols are hard to change. How often does the HTTP protocol change? Never. The language used for the code doesn’t matter in this case.
If the code is just inefficient, well, inefficient Rust is probably slower than efficient Python or JavaScript. Could the complexity of Rust have pushed the devs towards a simpler but less efficient solution that ends up being slower than garbage collected languages? I’m sure this has happened before, but I don’t know anything about the Lemmy code.
Or, again, maybe I’m just underestimating the amount of compute required to support 1500 users sharing a little bit of text and a few images?
The numbers are a little higher than you mention (currently ~3.2k active users). The server isn’t very powerful either, it’s now running on a dedicated server with 6 cores/12 threads and 32 gb ram. Other public instances are using larger servers, such as lemmy.world running on a AMD EPYC 7502P 32 Cores “Rome” CPU and 128GB RAM or sh.itjust.works running on 24 cores and 64GB of RAM. Without running one of these larger instances, I cannot tell what the bottleneck is.
The issues I’ve heard with federation are currently how ActivityPub is implemented, and possibly the fact all upvotes are federated individually. This means every upvote causes a federation queue to be built, and with a ton of users this would pile up fast. Multiply this by all the instances an instance is connected to and you have an exponential increase in requests. ActivityPub is the same protocol used by other federated servers, including Mastodon which had growing pains but appears to be running large instances smoothly now.
Other than that, websockets seem to be a big issue, but is being resolved in 0.18. It also appears every connected user gets all the information being federated, which is the cause for the spam of posts being prepended to the top of the feed. I wouldn’t be surprised if people are already botting content scrapers/posters as well, which might cause a flood of new content which has to get federated which causes queues to back up; this is mostly speculation though.
As it goes with development, generally you focus on feature sets first. Optimization comes once you reach a point a code-freeze makes sense, then you can work on speeding things up without new features breaking stuff. This might be an issue for new users temporarily, but this project wasn’t expecting a sudden increase in demand. This is a great way to show where inefficiencies are and improve performance is though. I have no doubt these will be resolved in a timely manner.
My personal node seems to use minimal resources, not having even registered compared to my other services. Looking at the process manager the postgres/lemmy backend/frontend use ~250MB of RAM.
For now, staying off lemmy.ml and moving communities to other instances is probably best. The use case of large instances anywhere near the scale of reddit wasn’t the goal of the project until reddit users sought alternatives. We can’t expect to show up here and demand it work how we want without a little patience and contributing.
Yup was just typing a comment to basically this effect. Federation adds a ton of overhead – you can still do things fairly efficiently, but every interaction having to fan out to (and fan in from!) many servers instead of like a single RDBMS is gonna cost you.
In all likelihood the code is not as efficient as it could be, but usually you get time to work those out gradually. A giant influx of users quickly turns “TODO: fix in the next six months” into “Oh god the servers are melting fuck fuck.”
That said, assuming the devs can get over this hump, I suspect using a compiled language will pay off long-term. Sure things will still be primarily IO-bound, but making things less CPU-bound is usually a good thing.
For some illustrative examples: Mastodon is in Ruby and hits dumb scaling limitations far more often than other fedi microblogs. Pleroma/Akkoma are Elixir (and BEAM is super well optimized for fast message passing/scaling/IO), Calckey (primarily Typescript) is moving some code to Rust, GoToSocial (Golang) is able to run in a fraction of the resources of Mastodon. The admins of one of the bigger tech instances recently announced they’re basically giving up on administrating Mastodon and are instead going to write a new server from scratch in a compiled language because it’s easier for them than scaling a Rails monolith.
TL;DR everything is IO-bound til it’s not.
I’m pretty sure the fediverse needs a new kind of node at some point. If we assume, that almost every larger instance is connected to almost every other larger instance directly, then there’s a ton of duplicated and very small messages.
There needs to be some kind of hub in-between to aggregate and route this avalanche. Especially if, like you wrote, every upvote is a message, the overhead (I/O, unmarshalling, etc) is huge.
You mean like centralizing the fediverse? Who hosts the hub? Who maintains it? In which country? Who pays for it?
Not a single hub, multiple ones.
Anyone can host a hub, federated instances can negotiate the intersection of hubs they both trust and then send traffic that way. That could mean, a single comment might be sent to, say, five hubs and each hub then forwards to 50 instances or so.
Since the hubs are rather simple, they can scale very easily and via cryptographic ratchets, all instances can make sure, they received the correct messages.
Hmm. Does the federation protocol only send information directly between servers, by that I mean that when something happens on A, does it send it to all other federated servers by itself?
If you could just proxy messages through other servers it would be an improvement. Essentially every instance would also be a hub. If you’re an instance A, connected to B and C, when B send you something you pass it onto C, instead of having C communicate with B directly.
In order to prevent spam you’d need whitelisting for the instances which you will act as a proxy for, and messages will have to be signed. Also, some protocol to discover the topology surrounding your server would be neat for optimizing delivery.
O(n*n) isn’t really scalable, so you either
a - have a small number of nodes total
b - have a small number of hubs with a larger number of leaf nodes.
Either way, there’s going to be some nodes that become more influential than others.
This is kinda how Usenet worked (well, still does). Rather than n*n federated connections, smaller providers tend to federate with central hubs that form backbones.
I think it makes sense for the fediverse as well.
Well said. Thanks for sharing your experience and those insightful links.
I can’t imagine what possessed them to use websockets other than “gee whiz websockets.”
Probably the same appeal as rust though: gee whiz.
What specs are you running your instance on?
An old Chromebox G1 (i7-4600U and 8GB of ram) with a 128GB internal NGFF and external 1TB NVMe. It’s by no means powerful or expensive hardware. I’m also only receiving federated posts for my subscribed communities, and sending out these comments I write so it’s a lot lower workload than the larger instances.
Hosting a personal instance is the direction I want to go. That’ll be a winter project, though. It’s fishing/swimming/rowing/woodworking season now :)
Are there any general tips or hidden dangers you can share? Or is it as straightforward as just putting my nose into documentation and playing around a bit?
Right now there appears to be no more danger than hosting anything else. I run my services behind a reverse proxy to insulate my home LAN from the wider internet, but hooking anything up to the internet comes with potential dangers.
I personally wrote a helm chart for kubernetes to host the service, and a few more can be found here. I have some work to do yet on the helm chart but it closely mirrors the docker-compose file provided by the developers. Deployment of an instance should be a lot easier soon, with all the increased interest and contributions.